GAN is an unsupervised learning approach that has been used in many applications for generating synthetic images, music, time series data, among others [1]}, [2]}, [3]}, [4]}, [5]}. GAN learns the distribution of a real dataset and generates synthetic data that are close to the real data [6]}. In the context of power systems, GAN-based methods have been developed to recover missing PMU data [7]} and generate synthetic PMU datasets [8]}, [9]}, [10]}.
